动态6G In-X子网的概率干扰预测

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Pramesh Gautam;Carsten Bockelmann;Armin Dekorsy
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引用次数: 0

摘要

由于动态移动、高密度部署和有限的可用信道带宽等因素的干扰,in - x子网在满足极端异构需求方面面临着巨大的挑战。为了解决这些挑战,我们引入了新的概率干扰预测技术,通过基于预测干扰分配资源来实现主动干扰管理,从而防止SNs的性能下降。然而,由于在传播环境、移动性和交通模式中与随机性相关的复杂、动态干扰,干扰预测具有挑战性。我们提出并评估了两类概率预测因子来模拟干扰的尾部统计:基于贝叶斯框架的变分推理稀疏高斯过程回归(VISPGPR)和用于直接分位数估计的分位数双向长短期记忆(QBiLSTM)预测因子。此外,我们还引入了一个基于matsamrin核的Student-t过程,以有效地捕获以异常值和快速波动为特征的干扰动态。该方法通过更好地建模重尾干扰分布,优于传统方法,包括使用常用的指数平方核和高斯先验的VISPGPR。然而,模型不匹配已被确定为最佳性能的主要瓶颈。为了解决这个问题,我们提出了一个基于注意力的改进QBiLSTM (attenm - mqbilstm),它通过利用时间相关性进一步提高了性能,而不需要对目标变量的潜在分布进行假设——这是VISPGPR没有捕捉到的一个方面。使用空间一致的3GPP信道模型,结合现实移动和两个不同的极端流量模型,评估了所提出的预测器的有效性。仿真结果表明,本文提出的预测方法在覆盖概率方面优于基线方法,在伯努利随机流量和确定性流量方面分别比VISPGPR提高了22-33%和4-13%。这有助于在两种极端流量建模场景中实现接近Genie资源分配(RA)的目标可靠性,同时确保预测干扰管理框架内的可伸缩性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probabilistic Interference Prediction for Dynamic 6G In-X Sub-Networks
In-X Subnetworks (SNs) face significant challenges in meeting extremely heterogeneous requirements due to interference resulting from dynamic mobility, hyper-dense deployment, and limited available channel bandwidth. To address these challenges, we introduce novel probabilistic interference prediction techniques that enable proactive interference management by allocating resources based on predicted interference, thereby preventing performance degradation in SNs. However, interference prediction is challenging due to complex, dynamic interference associated with randomness in the propagation environment, mobility, and traffic patterns. We propose and evaluate two categories of probabilistic predictors to model the tail statistics of interference: a Bayesian framework-based Variational Inference Sparse Gaussian Process Regression (VISPGPR) and a Quantile Bidirectional Long Short-Term Memory (QBiLSTM) predictor for direct quantile estimation. Additionally, we introduce a Matérn kernel-based Student-t process to effectively capture interference dynamics characterized by outliers and rapid fluctuations. This approach outperforms traditional methods, including VISPGPR with the commonly used exponential square kernel and Gaussian prior, by better modeling heavy-tailed interference distributions. However, model mismatch has been identified as a major bottleneck for optimal performance. To address this, we propose an Attention-based modified QBiLSTM (Atten-MQBiLSTM), which further enhances performance by leveraging temporal correlations without making assumptions about the underlying distribution of target variables-an aspect not captured by VISPGPR. The effectiveness of the proposed predictors is evaluated using a spatially consistent 3GPP channel model incorporating realistic mobility and two distinct extreme traffic models. Simulation results show that the proposed predictors outperform the baseline method in terms of coverage probability, with improvements ranging from 22–33% and 4–13% over VISPGPR for Bernoulli random and deterministic traffic, respectively. This helps achieve target reliability close to Genie resource allocation (RA) in both extreme traffic modeling scenarios while ensuring scalability within the predictive interference management framework.
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来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
期刊介绍: The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.
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